The hottest Software Development Substack posts right now

And their main takeaways
Category
Top Technology Topics

METR’s study on how AI affects developer productivity

Engineering Enablement • 19 implied HN points • 23 Jul 25
🕹 Technology Software Development
  1. Developers using AI tools actually took 19% longer to complete tasks, which is the opposite of what many people expected.
  2. Many developers were too optimistic about AI's benefits, even after experiencing a slowdown—they still thought it helped them a little.
  3. AI tools struggled with complex code and didn’t perform well for tasks where developers already had a lot of expertise.

The SPACE of AI: Real-world lessons on AI’s impact

Engineering Enablement • 15 implied HN points • 21 Aug 25
🕹 Technology Software Development
  1. Most developers believe AI makes them more productive, but its benefits can vary by task and team. Many developers feel AI tools help them work better, but not everyone sees the same improvement.
  2. Developers who frequently use AI are often more productive, especially with routine tasks. The more they use it, the better they get at knowing how to apply it effectively.
  3. Organizational support is key for AI adoption. Companies encouraging AI use see more of their developers using it daily and benefiting from its features.

Breaking Down the DoD Software Modernization Strategy

Resilient Cyber • 79 implied HN points • 13 Apr 23
🕹 Technology Software Development
  1. The Department of Defense (DoD) wants to modernize its software to keep up with technology and improve national security. They plan to deliver software that is reliable and fast to adapt to changing needs.
  2. A key part of the strategy is embracing cloud technologies and making sure software can withstand and recover from issues. This means investing in modern tech and improving processes to speed up software delivery.
  3. To achieve these goals, the DoD recognizes the importance of updating how it trains and manages its workforce. They need to make sure their team is skilled and ready to adapt to new technologies and ways of working.

sqlmesh cube_generate

davidj.substack • 59 implied HN points • 16 Dec 24
🕹 Technology Software Development
  1. Building integrations can seem tough, but understanding the metadata available can simplify the process. It's important to leverage existing tools to create new functionalities efficiently.
  2. Trying out new ideas, even if they might fail, is essential for learning and discovering possibilities. Taking small steps can help you manage potential setbacks.
  3. Creating a command to generate projects based on existing data models can streamline workflows. It allows for easier implementation of complex data relationships when set up correctly.
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Results of #1BRC: So what do we need Moonshots for? - JVM Weekly vol. 70

JVM Weekly • 19 implied HN points • 08 Feb 24
🕹 Technology Software Development
  1. Moonshots in technology are ambitious, groundbreaking initiatives inspired by the success of the Apollo 11 mission in 1969.
  2. Automatic differentiation of Java methods using Code Reflection allows for efficient mathematical function representations.
  3. Innovation in programming languages like Pkl and advancements in Java implementations like CheerpJ are shaping the future of technology.

Hurry up

davidj.substack • 23 implied HN points • 21 Jun 25
🕹 Technology Software Development
  1. Information security teams should be proactive instead of reactive. Companies need to adapt quickly as many vendors are now offering AI features that can affect data security.
  2. It's inefficient to have separate security evaluations for vendors that offer AI. Organizations should streamline the approval process as more tools will incorporate AI.
  3. Companies should provide approved AI tools for employees to use instead of denying access to popular non-corporate solutions. This way, they can maintain security while still allowing employees to leverage AI effectively.

Codesmithing

Peter's Newsletter • 39 implied HN points • 24 Apr 23
🕹 Technology Software Development
  1. AI-based tools are becoming better at programming, not just generating code.
  2. LLMs are making it easier for end-users to create their own software.
  3. Agents using code can improve themselves and autonomously work towards solving user requests.

Big and small knives

Optimism of the will • 39 implied HN points • 14 Apr 23
🕹 Technology Software Development
  1. You only need two knives: a big one and a small one for various tasks.
  2. AI can be like a big knife, efficient but not perfect; human thought is the small knife for precision.
  3. AI advancements allow for creating and consuming imperfect and unique content at reduced costs.

AI-Assisted Coding: My Experience as a Startup Founder

wentin’s newsletter • 39 implied HN points • 31 May 23
🕹 Technology Software Development
  1. AI can assist startup founders in various tasks like coding, content creation, and email management.
  2. Using AI tools like Copilot and ChatGPT can streamline code documentation and help in creating API documentation more efficiently.
  3. AI can be used to quickly generate proof-of-concept code for new features and even write an entire app from scratch, speeding up learning and development processes.

The Power of Constraints

The Serverless Mindset • 39 implied HN points • 02 Mar 23
🕹 Technology Software Development
  1. Setting strong deadlines can help prioritize and make hard choices when developing new features.
  2. Small, autonomous teams can be more effective in delivering products than larger teams.
  3. Embrace constraints and see them as a positive force that can lead to greater focus and innovation.

Measuring the Productivity of Developers

burkhardstubert • 39 implied HN points • 04 Oct 23
🕹 Technology Software Development
  1. McKinsey suggests measuring developer productivity using new metrics that track time spent on development versus other tasks. This way, they want to see more time in real coding and less in meetings.
  2. Orosz and Beck argue that measuring effort or output isn't very useful because people might manipulate those numbers. Instead, they say to focus on the actual effects of the work, like the value that reaches customers.
  3. Team performance is more important than individual effort. It's better to measure how well a team works together than to judge each person separately.

Why Kotlin is eating Java [Storytime Saturdays]

Technology Made Simple • 39 implied HN points • 09 Apr 23
🕹 Technology Software Development
  1. Big companies are increasingly adopting Kotlin over Java for their workflows due to its multi-functionality and great design.
  2. According to surveys, Kotlin has been consistently well-liked and is expanding beyond Android development to other platforms.
  3. Understanding why Google initially chose Java for Android, what issues Java presented, and what makes Kotlin appealing to various organizations can provide valuable insights for tech professionals.

Master The Business Benefits Behind Dependency Injection

🔮 Crafting Tech Teams • 39 implied HN points • 06 Aug 23
🕹 Technology Software Development
  1. Understanding dependency injection goes beyond configuring files and constructors; it has significant business benefits.
  2. Chaotic teams may focus on cash flow over profit, but mastering dependency injection can help streamline the business cycle and improve overall efficiency.
  3. For tech leads and managers, grasping the business impacts of dependency injection can lead to more effective decision-making and team management.

Edge 461: The Many Challenges of Kowledge Distillation

TheSequence • 56 implied HN points • 31 Dec 24
🕹 Technology Software Development
  1. Knowledge distillation can be tricky because there’s a big size difference between the teacher model and the student model. The teacher model usually has a lot more parameters, making it hard to share all the useful information with the smaller student model.
  2. Transferring the complex knowledge from a large model to a smaller one isn't straightforward. The smaller model might not be able to capture all the details that the larger model has learned.
  3. Despite the benefits, there are significant challenges that need to be tackled when using knowledge distillation in machine learning. These challenges stem from the complexity and scale of the models involved.

Corrective RAG (CRAG)

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 19 implied HN points • 05 Feb 24
🕹 Technology Software Development
  1. Corrective Retrieval Augmented Generation (CRAG) helps improve how data is used in language models by correcting errors from retrieved information.
  2. It uses a special tool called a retrieval evaluator to check the quality of the data and decide if it's correct, incorrect, or unclear.
  3. CRAG is designed to work well with different systems, making it easier to apply in various situations while enhancing document use.

Rethinking The Software Development Life Cycle

Beekey’s Substack • 2 HN points • 31 Jul 24
🕹 Technology Software Development
  1. The traditional waterfall model of software development rarely works well. Projects often go over budget, and the software can end up being unusable.
  2. Agile development was created to improve this, but many teams still stick to outdated processes and struggle with meeting user needs.
  3. Involving users early by writing code during requirements gathering can lead to better feedback and faster development, making sure the software created is valuable.

Episode 33: Better Built By Burkhard

burkhardstubert • 99 implied HN points • 01 Jan 23
🕹 Technology Software Development
  1. Test-Driven Development (TDD) helps developers get quick feedback while coding, improving overall project quality. This means fewer mistakes and less time spent fixing problems later.
  2. Using TDD can reduce the complexity of code by breaking down problems into smaller parts, making it easier to manage and understand.
  3. TDD encourages a culture of continuous improvement and teamwork, allowing all developers to take responsibility for the code they write. This leads to better collaboration and a more successful project.

sqlmesh model kinds - 1

davidj.substack • 59 implied HN points • 06 Dec 24
🕹 Technology Software Development
  1. There are different types of models in sqlmesh, such as full, view, and embedded models, each having unique functions and uses. It's important to choose the right model type based on how fresh or how often you need the data.
  2. SCD Type 2 models are useful for managing records that change over time, as they track the history of changes. This can make analyzing data trends much easier and faster.
  3. External models in sqlmesh allow you to reference database objects not managed by your project. This can simplify data modeling and documentation, as they automatically gather useful metadata.

I made 1+1=0 in DuckDB

VuTrinh. • 19 implied HN points • 03 Feb 24
🕹 Technology Software Development
  1. DuckDB is easy to use because it works like SQLite, running directly inside applications without needing a separate server. This makes it simpler to manage.
  2. It processes data in batches through vectorization, which means it can handle multiple records at once, making operations faster than traditional row-by-row processing.
  3. DuckDB supports ACID transactions, ensuring that data remains safe and reliable, which is important in data analytics and shared environments.

Breaking Into Tech

Jake [Building in NYC] • 19 implied HN points • 03 Feb 24
🕹 Technology Software Development
  1. To get a job in software engineering, you need to learn key technical skills like React, Typescript, and some backend basics. Focus on building small projects to practice what you've learned.
  2. Having good communication, flexibility, and grit is just as important as technical skills. Being open to learning and asking questions can really help you succeed in your first job.
  3. Networking and finding a mentor can make a big difference in breaking into tech. Building relationships and getting support from experienced people is key to finding job opportunities.

The Tech Buffet #23: What Nobody Tells You About RAGs

The Tech Buffet • 1 HN point • 22 Aug 24
🕹 Technology Software Development
  1. It's important to understand the business needs before jumping into building a Retrieval-Augmented Generation (RAG) system. Knowing the user's context and how they will use the system will save time and improve outcomes.
  2. Different types of data need to be indexed in specific ways for a RAG to work effectively. This means treating text, images, tables, and code differently to maximize the system's performance.
  3. The quality of the data chunks you use significantly affects the answers generated by a RAG. Taking the time to create clear, relevant chunks will lead to better responses from the system.

MultiHop-RAG

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 19 implied HN points • 31 Jan 24
🕹 Technology Software Development
  1. Multi-hop retrieval-augmented generation (RAG) helps answer complex questions by pulling information from multiple sources. It connects different pieces of data to create a clear and complete answer.
  2. Using a data-centric approach is becoming more important for improving large language models (LLMs). This means focusing on the quality and relevance of the data to enhance how models learn and generate responses.
  3. The development of prompt pipelines in RAG systems is gaining attention. These pipelines help organize the process of retrieving and combining information, making it easier for models to handle text-related tasks.

Edge 449: Getting Into Adversarial Distillation

TheSequence • 63 implied HN points • 19 Nov 24
🕹 Technology Software Development
  1. Adversarial distillation is a new model training method inspired by generative adversarial networks (GANs). It uses a setup where one part generates data and another part tries to tell if it's real or fake.
  2. This method helps improve knowledge transfer in models by combining typical distillation techniques with adversarial training. It's like guiding a student while testing their understanding.
  3. The process involves a generator that creates synthetic samples and a discriminator that distinguishes these samples from real ones, making learning more effective.

Under the microscope: Tony Hawk's Pro Skater 2 (PlayStation, Dreamcast)

Rings of Saturn • 58 implied HN points • 01 Dec 24
🕹 Technology Software Development
  1. There are new cheat codes in Tony Hawk's Pro Skater 2 that have been undiscovered for 24 years. This was found by someone analyzing the game and its cheat code system.
  2. A special Python script was created to discover these cheat codes by attacking the game's hashing system. This method used a combination of known button sequences and permutations.
  3. One of the new cheat codes allows players to turn off shadows in the game. Another doubles the score, which can make gameplay more interesting!

Big Tech and Generative AI Q3 '24 Update

Tanay’s Newsletter • 63 implied HN points • 04 Nov 24
🕹 Technology Software Development
  1. Amazon is making big strides in AI by providing tools for developers and creating custom chips. They are seeing huge interest in their AI services, which are growing fast despite lower profit margins.
  2. Google is using AI to improve its search capabilities and has rolled out new features to enhance user experience. Their AI models, called Gemini, are being adopted widely across their products and they are investing significantly in infrastructure.
  3. Apple has launched its AI system, Apple Intelligence, focusing on privacy and enhancing the user experience of their products. Although they're investing in AI, their spending is still lower compared to competitors, but they plan to increase their efforts.

Getting Faster for Your Own LLM Inference

The Beep • 19 implied HN points • 28 Jan 24
🕹 Technology Software Development
  1. Lowering the precision of LLMs can make them run faster. Switching from 32-bit to 16 or even 8-bit can save memory and boost speed during processing.
  2. Using prompt compression helps reduce the amount of information LLMs have to process. By making prompts shorter but still meaningful, the workload is lighter and speeds up performance.
  3. Quantization is a key technique for making LLMs usable on everyday computers. It allows big models to be more manageable by reducing their size without losing too much accuracy.

Tech Talks Weekly #4

Tech Talks Weekly • 19 implied HN points • 19 Feb 24
🕹 Technology Software Development
  1. The newsletter summarizes recent tech talks from various conferences, making it easier for readers to find valuable content. It's a great resource for anyone interested in technology.
  2. Each issue features a selection of must-watch talks, along with a list of new uploads categorized by conference. This helps viewers easily discover trending topics in tech.
  3. Readers are encouraged to provide feedback on the newsletter format and share it with friends or colleagues to grow the community. It's all about connecting more people to interesting tech discussions.

Don't abuse 0

CodeFaster • 144 implied HN points • 04 Jan 24
🕹 Technology Software Development
  1. Setting a spend limit of 0 in an API does not mean restricting spending to zero; it actually means allowing infinite spending.
  2. Consider using the string 'infinity' instead of '0' to denote unlimited spending.
  3. If needing to use an integer value for spend limits, consider using -1 to represent infinity, as it is not a common value and prompts further investigation.

Visualise & Discover RAG Data

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 19 implied HN points • 23 Jan 24
🕹 Technology Software Development
  1. RAGxplorer is a tool that helps visualize and explore data chunks, making it easier to understand how they relate to different topics.
  2. The process of Retrieval-Augmented Generation (RAG) involves breaking documents into smaller chunks to improve how data is retrieved and used with language models.
  3. Visualizing data can help identify problems like missing information or unexpected results, allowing users to refine their questions or understand their data better.